Efficacy and Safety of Anti-Tumor Necrosis Factor Alpha in Very Early Onset Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: Very early onset inflammatory bowel disease (VEOIBD) is defined as disease onset in patients younger than 6 years. Challenges in treatment of VEOIBD include lack of approved therapies and increased incidence of monogenic immunodeficiencies. We report on patterns of anti-TNF use, efficacy, and safety in a large cohort of patients with VEOIBD. METHODS: Very early onset inflammatory bowel disease patients receiving care at a single center were prospectively enrolled in a data registry and biorepository starting in 2012. Whole exome sequencing was available to all patients. Clinical data including IBD medication use and response were extracted from the medical record. We examined antitumor necrosis factor (anti-TNF) cumulative exposure and time to failure and evaluated the effect of covariates on anti-TNF failure using Cox proportional hazard regression. RESULTS: In this cohort of 216 VEOIBD patients with median 5.8-year follow-up, 116 (53.7%) were TNF-exposed. Sixty-two TNF-exposed patients (53.4%) received their first dose at younger than 6 years. Cumulative exposure to anti-TNF was 23.6% at 1 year, 38.4% at 3 years, and 43.4% at 5 years after diagnosis. Cumulative exposure was greater in patients with Crohn's disease (P = .0004) and in those diagnosed in 2012 or later (P < .0001). Tumor necrosis factor failure occurred in 50.9% of those exposed. Features predictive of anti-TNF failure included ulcerative colitis/IBD-unclassified (hazard ratio, 1.94; P = .03), stricturing (hazard ratio, 2.20; P = .04), and younger age at diagnosis (hazard ratio, 1.25; P = .01). Adverse events occurred in 22.6% of infliximab-exposed and 14.3% of adalimumab-exposed. CONCLUSIONS: Efficacy and safety of anti-TNFs in VEOIBD is comparable to what has previously been reported in older patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".